While artificial intelligence (AI) has been deployed in various functions across different industries over the past few decades, the world became truly in awe of the capabilities of the mechanical brain when generative AI burst onto the scene with ChatGPT in late 2022. Its uncanny ability to churn out full-length essays, passing examinations from the Wharton Business School (though inexplicably showing a dismal result at the Singapore Primary School Leaving Examinations) and seemingly able to hold a proper conversation with humans, ended up driving the fastest viral rate of adoption of any technology—taking just 60 days to reach 100 million users.
Generative AI’s relevance for use cases like customer service, marketing, and content generation is easy to see. But what potential lies in store for those in banking and financial services?
Corporate and investment banks (CIB) have been using AI and Machine Learning (ML) for decades, well before other industries caught on. Stock market traders have used ML models to derive and predict trading patterns, and natural language processing (NLP) has been deployed to trawl through tens of thousands of pages of unstructured data in securities filings and corporate actions to predict where a company might be headed.
AI vs. Gen AI: What’s different, and what are the opportunities?
One major difference between traditional AI and generative AI lies in the fact that traditional AI systems are primarily used to analyse data and make predictions, while generative AI goes a step further by creating new data from its training data. The former excels at pattern recognition, while the latter is primarily used in generating new patterns or content.
This makes banking and financial services one of the sectors that could see the biggest impact when it comes to revenue and cost efficiency from generative AI adoption. Three areas—new product development, customer operations, and marketing and sales—represent the most promising areas for Generative AI deployment in financial services.
For banks, this may include new product development, using generative AI to speed up software and app delivery using code assistants. In customer operations, banks can extract, search, and summarize unstructured data and translate it into legible instructions or analyses that can help inform further decision-making.
While global banking giants like JP Morgan put its sights on developing a ChatGPT-like digital investment advisor, local bank OCBC announced plans to deploy a generative AI bot for its 30,000 staff globally. During a six-month trial, OCBC deployed its generative AI bot to help with tasks like content generation, programming code, transcribing customer call centre voice calls, recording meeting minutes and summarizing financial reports. The results are promising. The 1,000 employees involved in the trial had reported completing their tasks in half the normal time needed with the bot, fact-checking included.
Marketing and sales is another area where generative AI has huge potential to transform the customer experience. In relationship management, generative AI has the potential to gather all voice and text interactions with clients and use them to create a digital “relationship manager (RM) assistant.” A generative AI–powered tool on the RM’s computer desktop can be used to help with tasks such as investment ideas, sales, and product policies nearly instantly. Marketers can also use the new tools to automatically summarize a bank’s knowledge and use it to create viable marketing content, such as market recaps, research reports, and pitch books. This smarter automation reduces time spent on research and enables marketers to spend more time engaging potential customers with timely and useful content.
Challenges and threats: Proceed with caution.
While the opportunities for deploying generative AI are immense, organizations will need to be mindful of and carefully plan for risks associated with scaling generative AI. Here are some of the major concerns that firms will need to address:
- Security threats: New applications may be subject to security vulnerabilities and manipulation.
- Privacy concerns: Generative AI may heighten privacy concerns through unintended use of client-sensitive information in model training, thus generating potentially sensitive outcomes.
- Computing cost: Deployment of generative AI at scale will require either using dedicated hardware or significantly increasing cloud workloads. This may result in high costs unless organizations work out a system to control runaway computing costs. This is especially so during times of economic uncertainty.
- ESG impact: Training and deployment of foundation models may increase carbon emissions, thus exceeding an organization’s sustainability commitments. Generative AI may also disrupt or in some cases displace workers, introducing reputational risk and potential talent shortages.
- Social Bias: Generative AI may project algorithmic bias because of imperfect training data or engineering decisions in both the development and deployment phases.
Looking Ahead: Evaluate where to innovate with Generative AI
Generative AI has immense power to shape the future of banking and financial services by enhancing customer experiences and business processes at scale across various functions. While use cases are still emerging on an almost daily basis, the biggest question to keep in mind is not ‘what’ to innovate but ‘where,’ as there will be a big difference in how suitable each opportunity is for a financial services institution.
All in all, with the right strategic approach to generative AI, financial companies can prioritize resources and effectively leverage this transformative technology to create a genuine competitive advantage with measurable impact. As with the immense creative power of generative AI, the opportunities for banks and financial institutions to deploy this emerging technology are endless.
